Visual tracking

Continuity-Aware Latent Interframe Information Mining for Reliable UAV Tracking

Object tracking is crucial for the autonomous navigation of unmanned aerial vehicles (UAVs) and has broad application in robotic automation fields. However, reliable aerial tracking remains a challenging task due to various difficulties like frequent …

SGDViT: Saliency-Guided Dynamic Vision Transformer for UAV Tracking

Vision-based object tracking has boosted extensive autonomous applications for unmanned aerial vehicles (UAVs). However, the frequent maneuvering flight and viewpoint change are prone to cause nerve-wracking challenges, e.g., aspect ratio change and …

Scale-Aware Siamese Object Tracking for Vision-Based UAM Approaching

In many industrial applications of unmanned aerial manipulator (UAM), visual approaching to the object is crucial to subsequent manipulating. In comparison with the widely-studied manipulating, the key to efficient vision-based UAM approaching, i.e., …

Siamese Object Tracking for Vision-Based UAM Approaching with Pairwise Scale-Channel Attention

Although the manipulating of the unmanned aerial manipulator (UAM) has been widely studied, vision-based UAM approaching, which is crucial to the subsequent manipulating, generally lacks effective design. The key to the visual UAM approaching lies in …

Ad2Attack: Adaptive Adversarial Attack on Real-Time UAV Tracking

Visual tracking is adopted to extensive unmanned aerial vehicle (UAV)-related applications, which leads to a highly demanding requirement on the robustness of UAV trackers. However, adding imperceptible perturbations can easily fool the tracker and …

Spatial Reliability Enhanced Correlation Filter: An Efficient Approach for Real-Time UAV Tracking

Traditional discriminative correlation filter (DCF) has received great popularity due to its high computational efficiency. However, the lightweight framework of DCF cannot promise robust performance when the tracker faces appearance variations …

DarkLighter: Light Up the Darkness for UAV Tracking

Recent years have witnessed the fast evolution and promising performance of the convolutional neural network (CNN)-based trackers, which aim at imitating biological visual systems. However, current CNN-based trackers can hardly generalize well to …

SiamAPN++: Siamese Attentional Aggregation Network for Real-Time UAV Tracking

Recently, the Siamese-based method has stood out from multitudinous tracking methods owing to its state-of-the-art (SOTA) performance. Nevertheless, due to various special challenges in UAV tracking, e.g., severe occlusion, and fast motion, most …

ADTrack: Target-Aware Dual Filter Learning for Real-Time Anti-Dark UAV Tracking

Prior correlation filter (CF)-based tracking methods for unmanned aerial vehicles (UAVs) have virtually focused on tracking in the daytime. However, when the night falls, the trackers will encounter more harsh scenes, which can easily lead to …

Mutation Sensitive Correlation Filter for Real-Time UAV Tracking with Adaptive Hybrid Label

Unmanned aerial vehicle (UAV) based visual tracking has been confronted with numerous challenges, e.g., object motion and occlusion. These challenges generally bring about target appearance mutations and cause tracking failure. However, most …